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Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques - Softcover
Synopsis
- Includes a solution manual for problems.
- Provides MATLAB code for examples and solutions.
- Deals with robust systems in both theory and practice.
"synopsis" may belong to another edition of this title.
Review
" well–organized very useful for a graduate level control or intelligent systems course " ( International Journal of Robust and Nonlinear Control, January 2005)
the text is well organised with topics judiciously selected to build on each other the discussion and motivations are rigorous (International Journal of Robust & Nonlinear Control, Vol.15, No.1, 10th January 2005)
"...this is an excellent book. It is pedagogically sound and, hence, suitable as a text for graduate courses.... I recommend it also as a very valuable resource to practitioners..." (International Journal of General Systems, Vol. 32, 2003)
From the Back Cover
A powerful, yet easy–to–use design methodology for the control of nonlinear dynamic systems
A key issue in the design of control systems is proving that the resulting closed–loop system is stable, especially in cases of high consequence applications, where process variations or failure could result in unacceptable risk. Adaptive control techniques provide a proven methodology for designing stable controllers for systems that may possess a large amount of uncertainty. At the same time, the benefits of neural networks and fuzzy systems are generating much excitement–– and impressive innovations–– in almost every engineering discipline.
Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques brings together these two different but equally useful approaches to the control of nonlinear systems in order to provide students and practitioners with the background necessary to understand and contribute to this emerging field.
The text presents a control methodology that may be verified with mathematical rigor while possessing the flexibility and ease of implementation associated with "intelligent control" approaches. The authors show how these methodologies may be applied to many real–world systems including motor control, aircraft control, industrial automation, and many other challenging nonlinear systems. They provide explicit guidelines to make the design and application of the various techniques a practical and painless process.
Design techniques are presented for nonlinear multi–input multi–output (MIMO) systems in state–feedback, output–feedback, continuous or discrete–time, or even decentralized form. To help students and practitioners new to the field grasp and sustain mastery of the material, the book features:
∗ Background material on fuzzy systems and neural networks
∗ Step–by–step controller design
∗ Numerous examples
∗ Case studies using "real world" applications
∗ Homework problems and design projects
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